SoI Distinguished Lecture

Date/Location: Thursday 15 February, 5pm in Appleton Tower LT5, followed by a reception in Appleton Tower

Abstract: AI technologies are being integrated into high-stakes applications such as self-driving cars, robotic surgeons, hedge funds, control of the power grid, and weapons systems. These applications need to be robust to many threats including cyberattack, user error, incorrect models, and unmodeled phenomena. This talk will survey some of the methods that the AI research community is developing to address two general kinds of threats: The "known unknowns" and the "unknown unknowns". For the known unknowns, methods from probabilistic inference and robust optimization can provide robustness guarantees. For the unknown unknowns, the talk will discuss three approaches: detecting model failures (e.g., via anomaly detection and predictive checks), employing causal models, and constructing algorithm portfolios and ensembles. For one particular instance of model failure---the problem of open category classification where test queries may involve objects belonging to novel categories---the talk will include recent work with Alan Fern and my students on providing probabilistic guarantees.

Bio: Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor Emeritus and Director of Intelligent Systems Research in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. Dietterich is one of the pioneers of the field of Machine Learning and has authored more than 190 refereed publications and two books. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development. He is best known for his work on ensemble methods in machine learning including the development of error-correcting output coding. Dietterich has also invented important reinforcement learning algorithms including the MAXQ method for hierarchical reinforcement learning.

Dietterich has devoted many years of service to the research community. He is Past President of the Association for the Advancement of Artificial Intelligence, and he previously served as the founding president of the International Machine Learning Society. Other major roles include Executive Editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and program chair of AAAI 1990 and NIPS 2000. Dietterich is a Fellow of the ACM, AAAI, and AAAS.